A Review on Using Crow Search Algorithm in Solving the Problems of Constrained Optimization

Authors

  • B. Surya Samantha   Department of Information Technology, CBIT, Hyderabad, Telangana , India
  • M. Trupthi  Department of Information Technology, CBIT, Hyderabad, Telangana , India
  • U. Sairam  Department of Information Technology, CBIT, Hyderabad, Telangana , India

Keywords:

Constrained Engineering Optimization, Metaheuristic Optimization, Crow Search Algorithm.

Abstract

This paper proposes a novel metaheuristic optimizer, named crow search algorithm (CSA), based on the wise conduct of crows. CSA is a population-based technique which works based on this thought crows store their abundance sustenance secluded from everything places and recover it when the nourishment is required. CSA is connected to improve six constrained engineering design problems which have diverse natures of target capacities, requirements and choice factors. Simulation results uncover that utilizing CSA may prompt finding promising results contrasted with alternate algorithms.

References

  1. Blum C, Roli A. Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 2003;35:268-308.
  2. Yang XS. Nature-inspired metaheuristic algorithms. Luniver Press; 2008.
  3. Yang XS. Engineering optimization: an introduction with metaheuristic applications. Wiley; 2010.
  4. Holland J. Adaptation in natural and artificial systems. Ann Anbor: University of Michigan Press; 1975.
  5. Geem ZW, Kim JH, Loganathan GV. A new heuristic optimization algorithm: harmony search. Simulation 2001;76:60-8.
  6. Yang X-S, Deb S. Cuckoo search via L_evy flights. In: Proceedings of world congress on nature & biologically inspired computing (NaBIC), Coimbatore, India; 2009. p. 210e4.
  7. Yang XS. A new metaheuristic bat-inspired algorithm. In: Gonzalez JR et al., editors. Nature-inspired cooperative strategies for optimization (NICSO 2010). Springer, SCI 284; 2010. p. 65-74.
  8. He S, Wu QH, Saunders JR. Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 2009;13:973-90.
  9. Yang XS. Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2010;2(2):78-84.
  10. https://en.wikipedia.org/wiki/Corvus_%28genus%29.
  11. Prior H, Schwarz A, Güntürkün O. Mirror-induced behavior in the magpie (pica pica): evidence of self-recognition. PLoS Biol 2008;6(8):e202.
  12. https://en.wikipedia.org/wiki/Hooded_crow.
  13. Clayton N, Emery N. Corvide cognition. Curr Biol 2005;15:R80-1.
  14. Wolpert DH, Macready WG. No free lunch theorems for search. Citeseer; 1995.
  15. Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Trans Evol Comput 1997;1:67-82.
  16. Ray T, Liew KM. Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput 2003;7:386-96.
  17. Liu H, Cai Z, Wang Y. Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 2010;10:629-40.
  18. Zhang M, Luo W, Wang X. Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 2008;178:3043-74.
  19. Sadollah A, Bahreininejad A, Eskandar H, Hamdi M. Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 2013;13:2592-612.
  20. Coello CAC. Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 2000;41:113-27.
  21. He Q, Wang L. An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 2007;20:89-99.
  22. He Q, Wang L. A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 2007;186:1407-22.
  23. Coelho LDS. Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 2010;37:1676-83.
  24. Parsopoulos K, Vrahatis M. Unified particle swarm optimization for solving constrained engineering optimization problems. Advances in natural computation LNCS, 3612. Berlin: Springer-Verlag; 2005. p. 582-91.
  25. Rao RV, Savsani VJ, Vakharia DP. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 2011;43:303-15.

International Journal of Scientific Research in Science and Technology

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Published

2018-02-28

Issue

Volume 4, Issue 2, January-February-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
B. Surya Samantha , M. Trupthi, U. Sairam, " A Review on Using Crow Search Algorithm in Solving the Problems of Constrained Optimization, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 2, pp.1374-1387, January-February-2018.